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About 10,000 papers for “Diffusion MRI”

Diffusion MRI

Zezhong Ye · OSF Preprints (OSF Preprints) · 2020

Cited by 0

Diffusion MRI

Oxford University Press eBooks · 2012

Since its initial development in the mid-1980s, and wide accessibility to perform diffusion MRI on all MRI scanners, the use of diffusion MRI has become widespread across the last 30 years. This online resource discusses the importance of ensuring that the hardware is performing optimally, the pulse sequence is carefully designed, the acquisition is optimal, the data quality is maximized while artifacts are minimized, the appropriate post-processing is used, and, where appropriate, the appropriate statistical testing is used, and the data are interpreted correctly. The author is a world authority on diffusion MRI, and has assembled most of the world's leading scientists and clinicians developing and applying diffusion MRI to produce a definitive, didactic and essential reference work for those working with diffusion MRI.

Cited by 64

Diffusion MRI

Zezhong Ye · OSF Preprints (OSF Preprints) · 2020

Cited by 0

Diffusion MRI

National Cancer Institute · Definitions · 2020

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Cited by 0Open access

Diffusion MRI

Kimball L. Christianson, Allen W. Song, Elmar M. Merkle et al. · Elsevier eBooks · 2012

Cited by 0

Diffusion MRI

Johansen-Berg, H, Behrens, TEJ · Oxford University Research Archive (ORA) (University of Oxford) · 2009

Cited by 0

diffusion MRI

Emmanuel Caruyer, Christophe Lenglet, Guillermo Sapiro et al. · 2013

Cited by 0

Diffusion MRI pipeline.

Harm J. van der Horn (3700717), Myrthe E. de Koning (3700714), Koen Visser (12559825) et al. · Figshare · 2023

<div><p>Research has shown that maladaptive personality characteristics, such as Neuroticism, are associated with poor outcome after mild traumatic brain injury (mTBI). The current exploratory study investigated the neural underpinnings of this process using dynamic functional network connectivity (dFNC) analyses of resting-state (rs) fMRI, and diffusion MRI (dMRI). Twenty-seven mTBI patients and 21 healthy controls (HC) were included. After measuring the Big Five personality dimensions, principal component analysis (PCA) was used to obtain a superordinate factor representing emotional instability, consisting of high Neuroticism, moderate Openness, and low Extraversion, Agreeableness, and Conscientiousness. Persistent symptoms were measured using the head injury symptom checklist at six months post-injury; symptom severity (i.e., sum of all items) was used for further analyses. For patients, brain MRI was performed in the sub-acute phase (~1 month) post-injury. Following parcellation of rs-fMRI using independent component analysis, leading eigenvector dynamic analysis (LEiDA) was performed to compute dynamic phase-locking brain states. Main patterns of brain diffusion were computed using tract-based spatial statistics followed by PCA. No differences in phase-locking state measures were found between patients and HC. Regarding dMRI, a trend significant decrease in fractional anisotropy was found in patients relative to HC, particularly in the fornix, genu of the corpus callosum, anterior and posterior corona radiata. Visiting one specific phase-locking state was associated with lower symptom severity after mTBI. This state was characterized by two clearly delineated communities (each community consisting of areas with synchronized phases): one representing an executive/saliency system, with a strong contribution of the insulae and basal ganglia; the other representing the canonical default mode network. In patients who scored high on emotional instability, this relationship was even more pronounced. Dynamic phase-locking states were not related to findings on dMRI. Altogether, our results provide preliminary evidence for the coupling between personality and dFNC in the development of long-term symptoms after mTBI.</p></div>

Cited by 0Open access

Diffusion MRI GAN

Vellmer · Zenodo (CERN European Organization for Nuclear Research) · 2024

Here we publish the models, scripts and Data of our publication 'Diffusion MRI GAN: Synthesizing Fibre Orientation Distribution Data Using Generative Adversarial Networks'.

Cited by 0Open access

Diffusion MRI GAN

Vellmer · Zenodo (CERN European Organization for Nuclear Research) · 2025

<p>Here we publish the models, scripts and Data of our publication '<em>Diffusion MRI GAN: Synthesizing Fibre Orientation Distribution Data Using Generative Adversarial Networks</em>'.</p>

Cited by 0Open access

Multidimensional diffusion MRI

Daniel Topgaard · Journal of Magnetic Resonance · 2016

Principles from multidimensional NMR spectroscopy, and in particular solid-state NMR, have recently been transferred to the field of diffusion MRI, offering non-invasive characterization of heterogeneous anisotropic materials, such as the human brain, at an unprecedented level of detail. Here we revisit the basic physics of solid-state NMR and diffusion MRI to pinpoint the origin of the somewhat unexpected analogy between the two fields, and provide an overview of current diffusion MRI acquisition protocols and data analysis methods to quantify the composition of heterogeneous materials in terms of diffusion tensor distributions with size, shape, and orientation dimensions. While the most advanced methods allow estimation of the complete multidimensional distributions, simpler methods focus on various projections onto lower-dimensional spaces as well as determination of means and variances rather than actual distributions. Even the less advanced methods provide simple and intuitive scalar parameters that are directly related to microstructural features that can be observed in optical microscopy images, e.g. average cell eccentricity, variance of cell density, and orientational order - properties that are inextricably entangled in conventional diffusion MRI. Key to disentangling all these microstructural features is MRI signal acquisition combining isotropic and directional dimensions, just as in the field of multidimensional solid-state NMR from which most of the ideas for the new methods are derived.

Cited by 233Open access

Diffusion-weighted MRI

Wilm, B · Zurich Open Repository and Archive (University of Zurich) · 2009

The jinc function is defined as a Bessel function of the first kind (J1) divided by its argument.

Cited by 0Open access

Diffusion MRI visualization

Thomas Schultz, Anna Vilanova · NMR in Biomedicine · 2018

Modern diffusion magnetic resonance imaging (dMRI) acquires intricate volume datasets and biological meaning can only be found in the relationship between its different measurements. Suitable strategies for visualizing these complicated data have been key to interpretation by physicians and neuroscientists, for drawing conclusions on brain connectivity and for quality control. This article provides an overview of visualization solutions that have been proposed to date, ranging from basic grayscale and color encodings to glyph representations and renderings of fiber tractography. A particular focus is on ongoing and possible future developments in dMRI visualization, including comparative, uncertainty, interactive and dense visualizations.

Cited by 22

Diffusion MRI GAN

Vellmer · Zenodo (CERN European Organization for Nuclear Research) · 2024

Here we publish the models, scripts and Data of our publication 'Diffusion MRI GAN: Synthesizing Fibre Orientation Distribution Data Using Generative Adversarial Networks'.

Cited by 0Open access

Diffusion MRI GAN

Vellmer · Zenodo (CERN European Organization for Nuclear Research) · 2024

Here we publish the models, scripts and Data of our publication 'Diffusion MRI GAN: Synthesizing Fibre Orientation Distribution Data Using Generative Adversarial Networks'.

Cited by 0Open access